2024年机器人与自动化国际会议（ IEEE ICRA 2024）于5月13日 - 5月17日在日本横滨举办。自主机器人实验室陈卫东教授、王贺升教授、王景川教授、张晗副教授团队共3篇论文在大会上发表。

 IEEE 国际机器人与自动化协会每年主办一次 IEEE 世界机器人与自动化大会（ IEEE InternationalConference on Robotics and Automation ，简称 IEEE ICRA )。 IEEE ICRA 是机器人领域规模（千人以上）和影响力均排名第一的顶级国际会议，是机器人领域权威研究人员介绍其研究成果的首要国际论坛。

论文信息

01.《K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters》

作者：Zehao Wang, Han Zhang, and Jingchuan Wang

摘要：Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

Tractor-trailer trajectory when tracking a given path

02.《Design and Optimization of an Origami-Inspired Foldable Pneumatic Actuator》

作者：Huaiyuan Chen, Yiyuan Ma, and Weidong Chen

摘要：A novel origami-inspired foldable pneumatic actuator is proposed in this letter to satisfy the comprehensive requirements in wearable assistive application. The pneumatic actuator combines the origami structure of designed Quadrangular-Expand pattern and the foldable pneumatic bellow. Integrated origami structure regulates the motion of actuator with high contraction ratio and enables accurate modeling. The origami framework also improves strength of bearing negative pressure, and thus can provide bidirectional actuation. The workflow including design, fabrication and mathematic modeling of the pneumatic actuator is presented in detail. Based on the actuator modeling, the multi-objective optimization for parameters using Genetic Algorithm is then conducted to obtain the trade-off design. The verifications for static characteristics of output torque, as well as the dynamic characteristics of power density, mechanical efficiency and frequency response, have been conducted. In summary, the proposed actuator is powerful and energy-efficient.

Illustration of the actuator in wearable assistive applications

03.《LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map》

作者：Xinrui Wu, Jianbo Xu, Puyuan Hu, Guangming Wang, and Hesheng Wang

摘要：Localization using a monocular camera in the prebuilt LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method.

Detailed pipeline of LHMap-loc